Cone Beam X-ray ray Luminescence Computed Tomography Based on Bayesian Method
Abstract: X-ray ray luminescence computed tomography (XLCT), which aims to achieve molecular and functional imaging by X X-rays, rays, has recently been proposed as a new imaging modality. Combining the principles of X X-ray ray excitation of luminescenceluminescence based probes and optical signal detection, XLCT naturally fuses functional and anatomical images and provides complementary information for a wide range of applications in biomedical research. In order to improve the data acquisition efficiency of previously developed narrow narrow-beam beam XLCT, a cone beam XLCT (CB(CB XLCT) mode is adopted here to take advantage of the useful geometric features of cone beam excitation. Practically, a major hurdle in using con cone e beam X-ray X for XLCT is that the inverse problem here is seriously ill ill-conditioned, conditioned, hindering us to achieve good image quality. In this paper, we propose a novel Bayesian method to tackle the bottleneck in CB CB-XLCT XLCT reconstruction. The method utilizes a local loc regularization strategy based on Gaussian Markov random field to mitigate the illill conditioness of CB-XLCT. XLCT. An alternating optimization scheme is then used to automatically calculate all the unknown hyperparameters while an iterative coordinate descent algorithm lgorithm is adopted to reconstruct the image with a voxelvoxel based closed-form form solution. Results of numerical simulations and mouse experiments show that the self self-adaptive adaptive Bayesian method significantly improves the CB-XLCT XLCT image quality as compared with conve conventional ntional methods.